ANN-Based STATCOM Tuning for Performance Enhancement of Combined Wind Farms

Abstract

Although the wind farms based on squirrel cage induction generators (SCIG) is cheaper than the wind farms based on doubly fed induction generators (DFIG), it is always in desperate need for reactive power compensation. Nevertheless, the wind farms based on DFIG are expensive compared with the SCIG wind farm, it features by its ability to control the active power independent of reactive power. However, combined wind farm (CWF) has been developed to collect the benefits of SCIG and DFIG wind turbines in the same wind farm. In this article, artificial neural network (ANN) is used to evaluate gain parameters of static synchronous compensator (STATCOM) in order to improve the stability performance of CWF. The impact of tuned STATCOM on the performance of CWF during gust wind speed and during three-phase fault is comprehensively investigated. The performance of CWF with STATCOM tuned by ANN is compared with its performance when the STATCOM tuned by the multiobjective genetic algorithm (MOGA) and whale optimization algorithm (WOA). The results show that the performance of CWF can be enhanced using STATCOM tuned by ANN more than MOGA and WOA.

Keywords:

• squirrel cage induction generators
• doubly fed induction generators
• combined wind farm
• STATCOM
• artificial neural network
• multiobjective genetic algorithm
• whale optimization algorithm

Additional information

Notes on contributors

Ahmed Rashad

Ahmed Rashad received the B.Eng. from Faculty of Energy Engineering, Aswan University, Egypt and M.Sc. degree in electrical power engineering from Faculty of Engineering, South Valley University, Egypt in 2013. He received the jointly-supervised Ph.D. degree in Department of Electrical Engineering, Aswan Faculty of Engineering, Aswan University, Egypt and University of Jaen, Spain in 2018. His research interests include wind energy and smart grid analysis.

Salah Kamel

Salah Kamel received the international Ph.D. degree from University of Jaen, Spain (Main) and Aalborg University, Denmark (Host) in January 2014. He is an Assistant Professor in Electrical Engineering Department, Aswan University. He is currently a Senior Research Fellow in State Key Laboratory of Power Transmission Equipment and System Security and New Technology, School of Electrical Engineering, Chongqing University, Chongqing, China. Also, He is a Leader for power systems research group in the Advanced Power Systems Research Laboratory (APSR Lab), Aswan, Egypt. His research activities include power system modeling, analysis and optimization, renewable energy and smart grid technologies.

Francisco Jurado

Francisco Jurado obtained the M.Sc. and Ph.D. degrees from the UNED, Madrid, Spain, in 1995 and 1999 respectively. He is Full Professor at the Department of Electrical Engineering of the University of Jaén, Spain. His research activities have focused on two topics: power systems: analysis and control, and renewable energy.

Mohamed Abdel-Nasser

Mohamed Abdel-Nasser received his B.Sc. and M.Sc. degrees in Electrical Engineering from Aswan University (Egypt) and his Ph.D. in Computer Engineering from Universitat Rovira i Virgili (Spain) in 2009, 2013, and 2016, respectively. He is currently an Assistant Professor at the Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan, Egypt. His research interests include the application of machine learning and deep learning to several real-world problems, including breast cancer detection, diabetic retinopathy image analysis, skin cancer detection, smart grid analysis and time-series forecasting.

Karar Mahmoud

Karar Mahmoud received the B.S. and M.Sc. degrees in electrical engineering from Aswan University, Aswan, Egypt, in 2008 and 2012, respectively. In 2016, he received the Ph.D. degree from the Electric Power and Energy System Laboratory (EPESL), Graduate School of Engineering, Hiroshima University, Hiroshima, Japan. Currently, he is working as an assistant professor in Aswan university. His research interests include modeling, analysis, control, and optimization of distributed systems with distributed generations.